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Predicting the price correlation of two assets for future time periods is important in portfolio optimization.
In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from twitter and analyze the correlation between stock market movements of a company and sentiments in tweets.
Stock market prediction is one of the most attractive research topic since the successful prediction on the market's future movement leads to significant profit.
Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction.
Along with the advance of opinion mining techniques, public mood has been found to be a key element for stock market prediction.
The model uses a target-sensitive representation of the news abstract to weigh sentences in the news content, so as to select and combine the most informative sentences for market modeling.
A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values.
Hence we outperform the single-feature setting in Fischer & Krauss (2018) and Krauss et al. (2017) consisting only of the daily returns with respect to the closing prices, having corresponding daily returns of 0. 41% and of 0. 39% with respect to LSTM and random forests, respectively.
SOTA for Stock Market Prediction on S&P 500